Choosing between man- and zone coverage is one of the most important strategic decisions a defensive coordinator has to take before each offensive play in American football. While experienced offensive coordinators and quarterbacks can often identify these defensive strategies visually, the growing availability of tracking data presents another opportunity to infer these defensive tactics. Previous approaches attempted to predict zone- or man coverage. One example can be seen in the below screenshot: here, display the Amazon Prime broadcast of the week 11 match between the Pittsburgh Steelers and Cleveland Browns of the current season includes the man- or zone prediction of Amazon’s NFL Next Gen Stats model. Although the full model and its specifies is not public, the live broadcast prediction mostly focus on specific plays without any pre-snap motion. In contrast, in this project, we include player movements after the line has been set, i.e. pre-snap motion, and exploit this additional information using hidden Markov models (HMMs). By modeling hidden states that represent the offensive player being guarded, we can infer the probabilities of state switches (i.e. whether defenders trail with offensive players or pass the responsibility on to their teammates). Implementing this information into an pre-motion detection model (such as the model by Amazon), we can show that the detection accuracy of the correct defensive strategy — man or zone coverage — is heavily increased. In this way, we provide a data-driven framework for assessing the efficiency of pre-snap motion, but also unravelling the complexity of defensive patterns, enabling real-time tactical insights for coaches.
Analyzing tracking data from nine weeks of the NFL 2022 season, we aim to forecast the defensive scheme (man- or zone defense). For this, we use the corresponding data from PFF that analysed every play and assigned the categories , and representing the different schemes. As it is not properly described what means, we omit every play that is associated with this value. Then, we end up with XY plays in total, from which the defense played Y in zone and X in man coverage.
Within these plays, we concentrate on the tracking data after the line has been set (because we are not interested in how players come out of the huddle) and before the ball has been snapped by the Center. For the HMM analysis, we further concentrate on those plays with pre-snap motion (ZZ plays).
To accurately forecast the defensive scheme (man- or zone defense) for every play, we need to create various features derived from the tracking data. In particular, we conducted the following feature engineering steps:
Our analysis comprises different steps:
We train a model to predict whether the defense plays a man- or zone coverage scheme. In particular, …..
The model uses the previously described features, blablabla.
We model the movements of defensive players during the phase of pre-snap motion within a hidden Markov framework, in which the underlying states represent the offensive players to be guarded (see Franks et al. 2015 for a similar approach in basketball). In contrast to Groom et al. (2024), who enforce a state to proxy zone coverage during corner kicks in soccer, we cannot proceed similarly as the classical coverage zones in American football will only be covered by the defenders post-snap.
The following video displays a touchdown from the Kansas City Chiefs against the Arizona Cardinals in Week 1 of the 2022 NFL season. We can see that, pre-snap, Mecole Hardman (KC #17) is in motion. He is immediately followed by the defender Marco Wilson (AZ #20), which is a clear indication for man-coverage.